mcpField
V
verdict-mcp

verdict-mcp

Local
github.com/DgotliebDeveloper Tools0Updated 9w ago

About verdict-mcp

verdict-mcp is an MCP server for coding agents that runs Python checks in isolated containers and returns compact, typed results. Failure fingerprints and history help distinguish new regressions from longstanding failures.

Key features

  1. 01Selects tests affected by working-tree changes
  2. 02Runs checks in isolated containers
  3. 03Returns typed verdicts and failure fingerprints
  4. 04Retrieves full tracebacks on demand
  5. 05Tracks when failures were first and last seen

Use cases

  1. 01Verify changes without running the entire test suite
  2. 02Identify whether a test failure predates a change
  3. 03Run ruff and mypy with normalized results

MCP support

The MCP server exposes tools for impact-selected verification, on-demand failure explanations, fingerprint history, and lint or type checks.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"dgotlieb-verdict": {
"command": "uvx",
"args": [
"verdict-mcp"
],
"env": {
"VERDICT_PROJECT": "<VERDICT_PROJECT>"
}
}
}
}
Run commanduvx verdict-mcp
Environment variables to set
VERDICT_PROJECTPath to the project to verify (defaults to cwd)

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypiverdict-mcp0.1.0a4

Tools

4

Saved tool list · updated 3w ago

ParameterTypeRequiredDescription
baseanyNogit ref to diff against (default: HEAD, i.e. uncommitted changes).
scopeanyNoNone for impact-based selection (default), 'all' for the full suite, or a path like 'tests/test_api.py' to run one file.

Details

From the project README

verdict

https://github.com/user-attachments/assets/62923912-98aa-4246-8bc8-4adf55ae3ff8

CI PyPI Python License

Structured, sandboxed verification feedback for coding agents.

An MCP server that replaces your agent's pytest shell-outs with something built for the agent inner loop: impact-selected tests, run in an isolated environment, returning compact typed verdicts instead of 40,000 tokens of raw runner output — with failure fingerprints that tell the agent whether a failure is its regression or was broken all along.

raw pytest dump:  ~40,000 tokens, unstructured, run un-sandboxed on your machine
verdict:              ~400 tokens, typed JSON, run in a rootless container, with memory

Why

The highest-frequency tool call in agentic coding is verification — and it's the least structured. Agents re-run whole suites when one module changed, burn context parsing ANSI-coded tracebacks, run arbitrary code directly on your machine, and routinely misdiagnose pre-existing breakage as their own regression (then "fix" code that wasn't broken). verdict fixes all four.

Tools

ToolWhat it does
verify(scope?, base?)Selects tests affected by your working-tree diff (static import graph via grimp), runs them via podman/docker with the worktree mounted read-only, returns typed failures with fingerprints and a preexisting flag
explain_failure(check_id)Full traceback for one failure, on demand — bulk never rides in the summary
history(fingerprint)First seen / last seen / times seen — regression vs. long-standing breakage
run_checks(["ruff","mypy"])Lint and type checks, normalized into the same verdict schema

Every failure carries a fingerprint: a stable hash of the normalized failure signature (volatile tokens — addresses, tmp paths, ids, durations — collapsed). Same logical failure, same fingerprint, across runs and refactors. Fingerprints are what give verdict memory.

Quickstart

No install step needed — uvx fetches it on first use. (Or uv tool install verdict-mcp / pip install verdict-mcp for a permanent verdict-mcp command.)

Claude Code — .mcp.json in your project root:

{
  "mcpServers": {
    "verdict": {
      "command": "uvx",
      "args": ["verdict-mcp"],
      "env": { "VERDICT_PROJECT": "." }
    }
  }
}

Cursor — same shape in .cursor/mcp.json.

Optional verdict.toml in your repo root:

[project]
packages = ["your_package"]          # for impact selection (auto-guessed if omitted)

[runner]
image = "ghcr.io/you/yourproj-test"  # prebuilt env with your deps
setup_cmd = "pip install -e .[test]" # or install on the fly (runs with network; tests don't)
# prefer = "local"                   # escape hatch if you have no container runtime

[limits]
max_failures = 10

Try it without an agent:

cd examples/demo_project
VERDICT_PROJECT=. verdict-mcp   # then connect any MCP client, or use the MCP inspector

Sandbox posture (v0.1)

Checks run in an ephemeral container (podman preferred, docker fallback): worktree mounted read-only at /src, copied to a writable /work inside the container, --network=none for the check run. Your host environment is never mutated by a test run. If setup_cmd is configured, that step runs with network before the check; prefer a prebuilt image for a tighter posture. No container runtime → explicit prefer = "local" fallback runs checks against a temp copy of your worktree (still never in place). See SECURITY.md for the full threat model and known limitations.

Troubleshooting: if a verdict says container engine 'podman' could not start the check, run the suggested podman pull <image> by hand — the engine's own error is the answer. One known trap on macOS: a "credsStore": "gcloud" line in ~/.docker/config.json makes podman call the gcloud credential helper for every registry, including docker.io; an expired gcloud login then breaks all pulls. Fix with gcloud auth login or remove that line.

Honest limitations

  • Impact selection uses the static import graph — approximate by design. Dynamic imports, fixture-by-name resolution, and data-driven tests can be missed; verify(scope="all") is always available and verdict says in selection_note whenever it falls back.
  • Python/pytest only today, plus ruff/mypy. The adapter interface is small and documented — vitest and go test -json adapters are the most-wanted contributions (CONTRIBUTING.md).
  • Flake detection and coverage-map-based selection are v0.2 (roadmap).

Roadmap

v0.2: coverage-based impact maps (precise selection), flake detection via fingerprint alternation, devcontainer.json support, result cache keyed on (tree hash, check, image digest). Later: vitest/jest, go test, cargo test adapters; per-repo verdict daemon mode.

License

Apache-2.0

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.